The long-term goal of this project is to develop and implement a computational methodology for virtual screening and design of new covalent transition-state (TS) analog enzyme inhibitors. The conventional computer assisted drug design (CADD) methodologies focus on optimization of the enzyme-inhibitor noncovalent recognition interactions. Unfortunately, such a well optimized in vitro recognition of non-covalent inhibitors is rapidly lost in vivo due to the development of mutational drug resistance cases of infectious diseases and cancer. The fact that catalytic residues of enzymes are not subjected to mutations led us to hypothesize those covalent TS analog inhibitors, having significant binding contributions from interactions with catalytic residues, should suffer less from mutational resistance. Thus, a computational tool to handle and design covalent TS analog inhibitors is of major importance. We have considered a special class of isoselective transition state analog inhibitors a set with identical recognition site (RS) and different chemical site (CS). The proposed project is based on the hypothesis that the trend of binding affinity to a target enzyme in a series of such inhibitors can be predicted, by a high-level quantum mechanical model- Enzyme Inhibitor Trend Analysis (EITA). This hypothesis is based on our earlier studies that analyzed the energetic contribution of various factors to the stability of the enzyme-inhibitor complex. We have previously applied an interdisciplinary computational/experimental approach in the study of enzyme catalysis and inhibition mechanisms. The models included the relevant chemical reaction center and a special protocol accounting for the protein/water environmental effect. The calculated models were calibrated by experimental data, to provide realistic and accurate results. Based on these findings, we suggest developing a methodology for the design of new reversible covalent enzyme inhibitors. Our research will focus on inhibitors of enzymes of the hydrolases superfamily. First we will examine and validate the proposed model with various medicinally-important enzymes. The model will account for the enzyme-inhibitor covalent bond and for the solvent-inhibitor competing reaction by high-level quantum mechanical DFT calculations and the results will be correlated with experimental kinetic data. We will then expand the screening abilities by taking into account also non-covalent interactions of the CS. The results will be presented as a database that could be used as an information source or as a tool for drug design. The mature algorithm and the database will ultimately be incorporated, in our future project, into a software package, with emphasis on simplicity and clarity for routine use of practical drug design. The methodology can be beneficial in the design of new lead compounds, as well as in the development of drugs that will not lose their activity due to target enzyme mutations. PUBLIC HEALTH RELEVANCE: This project aims to validate and further develop a computational tool to predict binding affinities and to perform virtual screening of new covalent transition-state (TS) analog enzyme inhibitors. The current project will result in a publicly available algorithm and a database comprised of various parameters that characterize binding trends between medicinally important targets and their inhibitors. Ultimately our method will be developed into a software package that will assist researchers in the design of novel drugs that may be less susceptible to mutational drug resistance.